University of Michigan, Winter 2010

Section 1MW 9-10:30 (EECS 1303), F 9:30-10:30 (EECS 1303) Instructor: Clayton Scott Office: 4433 EECS Office hours: M/F 1-2:30 |
Section 2MW 9-10:30 (EECS 3427), F 9:30-10:30 (EECS 3427) Instructor: Sandeep Pradhan Office: 4240 EECS Office hours: M/Th 2:30-4 |

__Required text__: *Introduction to Probability*, Bertsekas and
Tsitsiklis, 1st or 2nd edition. Note that this book has a website. On that
website are solutions to all of the book's exercises, as well as
additional excercises without solutions.

__Books on reserve__ at the library

- Sheldon Ross,
*A First Course in Probability* - Peter Olofsson,
*Probability, Statistics, and Stochastic Processes* - Sheldon Ross,
*Introduction to Probabilty Models* - Richard Hamming,
*The Art of Probability: For Scientists and Engineers* - A. Drake,
*Fundamentals of Applied Probability* - D. Stirzaker,
*Elementary Probability* - H. Stark and J. Woods,
*Probability and Random Processes with Applications to Signal Processing* - C. Helstrom,
*Probability and Stochastic Processes for Engineers* - A. Leon-Garcia,
*Probability and Random Processes for Electrical Engineering* - R. Yates and D. Goodman,
*Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers*

__Topics__:

Axioms of probability, counting, conditional
probability, independence, Bayes rule, discrete and continuous random
variables, expectation, joint distributions, statistical inference, random
processes (Chapters 1-4 and parts of the remaining chapters). We will also
cover applications in EECS including communication,
signal processing, and reliability theory.

__Prerequisites__:

- EECS 216 or an equivalent linear systems course.
- Familiarity with basic mathematical abstractions such as sets and functions.
- Calculus (differentiation and integration).

__Grading__:

Homeworks: 25%

Exam 1: 25% (Tues. Feb. 9, 6-8 PM, Dow 1013)

Exam 2: 25% (Tues. March 16, 6-8 PM, GGBrown 1504)

Exam 3: 25% (Tues., April 27, 1:30 PM - 3:30 PM, location TBA)

The final grade will be set on a curve, with the median grade (among undergraduates) as a B. Graduate students will be graded on a different curve.

__Exams__

All exams will be closed book/notes and last for 2 hours. Each exam will
mostly cover
material since the previous exam, but understanding of earlier material
will still be expected. Both sections will take the same exams at the same
time.

__Homeworks__:

Homeworks will be assigned once per week (except for exam weeks) and due
on Tuesdays
at 5pm. Your lowest homework score will be dropped. All homeworks will
carry equal weight. Both sections will be assigned the same homeworks.
Late homeworks will not be accepted except in special cases such as a
serious illness.

__Collaboration__:

All homework assignments are to be completed on your own. You are allowed
to consult with other students during the conceptualization of a solution,
but all written work, whether in scrap or final form, is to be generated
by you working alone. You are also not allowed to use, or in any way
derive advantage from, solutions prepared by other students, or in prior
years.

__Honor Code__:

All undergraduate and graduate students are expected to abide by the
College of Engineering Honor Code as stated in the Student Handbook and
the Honor Code Pamphlet.

__Students with Disabilities__:

Any student with a documented disability needing academic adjustments or
accommodations is requested to speak with their instructor during the
first two weeks of class. All discussions will remain confidential.

The study of probability, statistics, and random processes can be fun and rewarding, but it may also require patience and perseverance. You will need to develop new critical thinking skills. Unlike some undergraduate engineering courses, you will not get through the course by simply memorizing a few key formulas. There is some structure to the different problem solving methods, and we will do our best to explain that structure, but many problems will not fit into a nice clean mold. Because of this, we offer the following advice:

- The only way to learn the material is to solve problems, including those not assigned as homework.
- Do not expect to be able to start the homeworks the day before they are due and receive a good grade in the class. You may need to attack a problem from several different angles before you see a solution.
- Success in this course is proportional to perseverance. Ask yourself: How long are you willing to work on a problem before deciding you can't solve it? The longer you are willing to work on a problem, the greater your chance of solving it, and therefore the greater your chance of learning.